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 Educators who teach science using R tend to face common pedagogical problems even across different scientific domains. Yet instructors who teach with R often feel isolated at their institutions. They may be the only ones in their departments to teach using R; even if there are others, there tends not to be a culture of collaboration around teaching materials the way that there is around research. In this three-part series of blog posts, participants at the rOpenSci 2018 unconf will briefly survey the state of teaching science with R. This first post aims to summarize the main challenges that educators face, as a tool to help them think through the decisions they make about their course materials. The second post will explain what makes for a good educational resource which can address these shared challenges. The final post will sketch out the main things that educators can do in the future to create and share teaching materials and—even more important—to foster a community of practice around teaching science with R.
 
-The primary challenge instructors face is how to keep the focus on their subject matter and not on R. In most courses that use R, some subject like science or statistics or history is the main thing thing to be taught, and teaching R is only a means to that end. In other words, we are not teaching courses about R programming or programming in general, as one might in computer science. Rather we are teaching R so that we can teach specific skills, such as modeling, manipulating, or visualizing data. And we teach those skills so that we can teach students how to think as biologists, ecologists, psychologists, social scientists, or historians. Yet much of the day to day work of teaching and learning is necessarily focused on R, since in these domains R is not just a programming language but actually a language of thought and expression within one’s discipline. Educators therefore must balance teaching R with teaching their subject.
+The primary challenge instructors face is how to keep the focus on their subject matter and not on R. In most courses that use R, some subject like science or statistics or history is the main thing to be taught, and teaching R is only a means to that end. In other words, we are not teaching courses about R programming or programming in general, as one might in computer science. Rather we are teaching R so that we can teach specific skills, such as modeling, manipulating, or visualizing data. And we teach those skills so that we can teach students how to think as biologists, ecologists, psychologists, social scientists, or historians. Yet much of the day to day work of teaching and learning is necessarily focused on R, since in these domains R is not just a programming language but actually a language of thought and expression within one’s discipline. Educators therefore must balance teaching R with teaching their subject.
 
 This challenge leads into the fundamental question of course design: scoping and sequencing. What things should be taught, and in what order? In a course that teaches a discipline with R, questions of scoping necessarily involve which parts of R should be taught. Will the course be primarily focused on the tidyverse, or will it mostly use base R? If the tidyverse is taught, at what point are base R conventions taught so that students have access to the whole R ecosystem? Much of this decision depends on the characteristic data structures for the field of study: for example, can tabular data structures (i.e., data frames) be used or must students also be taught how to deal with matrices, lists, or some other data structure less amenable to the tidyverse?